Blood Glucose Meter And Computer-Implemented Method For Facilitating Accurate Glycemic Control By Modeling Blood Glucose Using Circadian Profiles

ABSTRACT

A blood glucose meter and computer-implemented method for facilitating accurate glycemic control by modeling blood glucose using circadian profiles is provided. Anti-hyperglycemic medications are categorized based on similar glucose lowering effects. A circadian profile is built by collecting at least two recent typical measurements of pre-meal and post-meal period data stored on a glucometer, identifying a dose of an anti-hyperglycemic medication, and identifying the class of the anti-hyperglycemic medication. A model of glucose management through the circadian profile is created by estimating expected blood glucose values and their predicted errors at each of the meal periods, visualizing the expected blood glucose values and their predicted errors over time for each meal period in a log-normal distribution, and selecting one of the meal periods and, for each anti-hyperglycemic medication in the identified class, modeling a change in the dose of the anti-hyperglycemic medication.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 13/559,552, filed Jul. 26, 2012, pending, the disclosure ofwhich is incorporated by reference.

FIELD

The present invention relates in general to glycemic management indiabetic patients and, in particular, to a blood glucose meter andcomputer-implemented method for facilitating accurate glycemic controlby modeling blood glucose using circadian profiles.

BACKGROUND

As a chronic and incurable disease, diabetes mellitus requirescontinuing care that lasts throughout the life of the patient. Bothcaregivers and patients alike are expected to play an active role inmanaging diabetes, regardless of form, whether Type 1, Type 2,gestational, or other. Diabetes patients are typically coached by theircaregivers on lifestyle modification and educated to understand theaffects of diet, especially carbohydrates, body weight, physicalactivity, medications, and stress on their diabetic condition. Diabetespatients are also trained and encouraged to regularly test their bloodglucose levels with the assistance of a portable glucose meter(“glucometer”). In addition, medication-treated patients learn toundertake daily self-administration of medications and, whereappropriate, determine corrective medication dosing to counteractpostprandial glycemic rise. All diabetes patients are expected todocument their self-care in a daily diary that typically chroniclestheir daily self-monitored blood glucose values, medications, physicalactivity, and dietary intake.

In turn, caregivers follow their diabetes patients on a periodic basisand work to ensure their compliance with the consensus guidelines andmandatory targets (CG&MT), which have been formulated and are regularlyupdated by the American Association of Clinical Endocrinologists (AACE)and the American College of Endocrinology (ACE), as well as the AmericanDiabetes Association (ADA). At each patient consultation, a caregivermay evaluate the patient's daily diary to identify patterns in thepre-meal data, which can include examining particular examples of thepatient's actions to determine underlying causes for any outcomessuffered, above all, episodes of hypoglycemia. Additionally, thecaregiver will normally test the patient's level of glycated hemoglobin(“A1c”) to establish accord with the current CG&MT target forwell-managed diabetes. As needed, the caregiver may adjust the patient'soral anti-diabetic medications or insulin dosing to hopefully move thepatient's blood glucose and A1c levels closer to the mandated targets.

The roles respectively performed by caregivers and their diabeticpatients form a “circle of care” that requires each patient to providetheir own data and do those actions necessary that together allow thecaregivers to effectively manage the patient's diabetic condition. At aminimum, each patient is expected to self monitor their blood glucoselevels and comply with each caregiver's instructions. Obversely, thecaregivers are expected to monitor the patient's condition and provideapt guidance through changes in medications and lifestyle as needed toachieve perfect diabetes control as mandated in the various guidelines.

Notwithstanding, the circle of care generally remains incomplete.Conventional diabetes management efforts are in practice remarkablyretrospective due to the significant focus on past patient condition, asseen through the patient's self-monitored blood glucose values thatordinarily extend back over several prior months. In turn, armed at bestwith the historical values of blood glucose testing, as sometimesconfirmed by A1c results, a caregiver endeavors to control the futuredirection of ongoing diabetes treatment typically for the next severalmonths until the next consultation. This control is exercised chiefly bymaking adjustments to medications, typically focused on insulin, withthe intent of somehow moving patient blood glucose levels and A1c totarget, and often without demanding data more reflective of thepatient's true condition at the time of consultation.

Some types of conventional glucometers attempt to bridge the gap in thecircle of care by assisting patients in their own between-consultationdiabetes self-management efforts. Basic glucometers merely calculate anddisplay blood glucose levels by reading a disposable test strip uponwhich the patient has placed a drop of blood. However, so-called “smart”glucometers typically include an internal memory that electronicallyrecords the results of each blood glucose test, along with the date andtime of testing. The stored blood glucose data can then be madeavailable for download to a personal computer. In addition, whenavailable, onboard software can calculate an average of recent bloodglucose levels and identify trends and patterns in the blood glucosedata. Notwithstanding, the decision on whether diabetes medicationdosing or other changes are appropriate remains solely at the discretionof the patient or follow-on caregiver consultation.

The incompleteness of the circle of care contributes to the dilemmafaced by caregivers in managing diabetes, which suggests thatsatisfactory glycemic control is seemingly only achievable withunsatisfactory risk of hypoglycemia, as well as the converse. The CG&MTrecommends a fasting blood glucose level of less than 110 mg/dL(non-fasting less than 140 mg/dL) and A1c between 6% and 7%, withpatients generally being asked to strive for A1c of less than 7% (andless than 6.5% according to other standards). Achieving these goals,however, carries the adverse consequence of increasing the risks oftreatment-related hypoglycemia, which caregivers counter by changingdiet or medication dosing that then shifts that patient's blood glucoselevel outside the CG&MT target range. Consequently, a self-reinforcingvicious cycle is formed, as increased medication dosing to reduceglycemic values into mandated target ranges results in increasedhypoglycemic risk that a patient must counteract by eating more with anensuing gain in body weight that induces further diabetes medicationdosing change.

Therefore, a need remains for providing an improved approach to glycemiccontrol that shifts the focus of diabetes management efforts away fromretrospective blood glucose histories, such as stored on a glucometer,to recent and representative glycemic indications that better tiecaregiver efforts and glucose management to actual, realized and timelypatient need.

SUMMARY

A portable glucometer implements a patient database that organizes theresults of SMBG testing into a circadian profile. The circadian profileis offloaded and processed to accurately model expected blood glucosevalues and their expected error by using only the SMBG data stored in anear-term observational time frame, typically a week, immediatelypreceding the next caregiver consultation. Only validated (recent andtypical) SMBG values are used in predicting expected glycemic outlook,thereby ensuring a reliable model. The caregiver can then explorechanges to medication dosing, which can include all manner ofanti-diabetes drugs, including insulin and oral agents, with confidencethat the new dosing will both move the patient's glycemic control intothe desired target ranges and avoid the deleterious risk oftreatment-related hypoglycemia.

One embodiment provides a blood glucose meter and computer-implementedmethod for facilitating accurate glycemic control by modeling bloodglucose using circadian profiles. A plurality of meal periods that eachoccur each day are defined. A plurality of classes that each classcomprises a set of anti-hyperglycemic medications that has similarglucose lowering effects are defined. A circadian profile for a diabeticpatient is built by choosing an observational time frame for thecircadian profile comprising a plurality of days that have occurredrecently, collecting at least two sets of pre- and post-meal period datathat were recorded at each of the meal periods that occurred each day inthe observational time frame and stored on a glucose meter, reading alevel of blood glucose on a test strip provided to the glucose meter bythe diabetic patient for each of the meal periods, identifying a dose ofan anti-hyperglycemic medication that was taken during each of the mealperiods as the reading of the blood glucose level, identifying the classto which the anti-hyperglycemic medication is comprised, and storing theblood glucose level and the anti-hyperglycemic medication dose into thecircadian profile in a record for each of the meal periods. A model ofglucose management through the circadian profile for the diabeticpatient for each of the anti-hyperglycemic medications in the identifiedclass is created by defining a modeling period comprising a plurality ofdays, which each comprise the same plurality of the meal periods thatoccurred each day in the observational time frame, estimating expectedblood glucose values and their predicted errors at each of the mealperiods from the blood glucose level in each record based on the mealperiods in the circadian profile, visualizing the expected blood glucosevalues and their predicted errors over time for each meal period in alog-normal distribution, selecting one of the meal periods and, for eachanti-hyperglycemic medication in the identified class, modeling a changein the dose of the anti-hyperglycemic medication for the selected mealperiod. Further, the modeling the change in the dose of theanti-hyperglycemic medication includes obtaining glucose lowering effectof the modeled change in the dose of the anti-hyperglycemic medicationand glucose lowering effects of other anti-hyperglycemic medications inthe determined class, normalizing the glucose lowering effect of themodeled change in the dose of the anti-hyperglycemic medication with theglucose lowering effects of the other anti-hyperglycemic medications inthe determined class, propagating the normalized glucose lowering effectover time for the modeled change in the dose of the anti-hyperglycemicmedication to the expected blood glucose values, beginning with theselected meal period and continuing with each of the meal periodsoccurring subsequently in the modeling period, the normalized bloodglucose lowering effect being adjusted in proportion to each subsequentmeal period until the normalized blood glucose lowering effect isexhausted, and visualizing the expected blood glucose values aspropagated and their predicted errors in the log-normal distribution.

For certain types of diabetes patients, the approach removes the needfor repeated SMBG testing throughout each day and extending over theentire course of time separating caregiver consultations. Type 2diabetes patients, for instance, would only need to collect a minimum oftwo SMBG results per meal period in the week prior to consultation.Moreover, with this approach, glycemic management can be performed in anintermittent “batch” processing fashion and not in real time.

The approach also enhances caregiver confidence, as the predicted bloodglucose levels and their error ranges are based on recent and typicalpatient data. The caregiver is then able to treat to target and safelyprescribe medication dosing changes, which can include all manner ofanti-diabetes drugs, including insulin and oral agents, with a highdegree of confidence of attaining the results desired.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a blood glucose meter for improvingglucose management through modeling of circadian profiles, in accordancewith one embodiment.

FIG. 2 is a flow diagram showing a computer-implemented method forimproving glucose management with a glucometer through modeling ofcircadian profiles, in accordance with one embodiment.

FIG. 3 is a flow diagram showing a routine for collecting meal perioddata into the circadian profile stored on the glucometer for use in themethod of FIG. 2.

FIG. 4 is a user interface diagram showing, by way of example, aninteractive screen for a circadian profile for use in the system of FIG.1.

FIG. 5 is a flow diagram showing a routine for visualizing expectedblood glucose values and predicted errors for use in the method of FIG.2.

FIG. 6 is a flow diagram showing a routine for superimposing targetranges over the expected blood glucose values and predicted errors foruse in the method of FIG. 2.

FIG. 7 is a user interface diagram showing, by way of example, aninteractive screen for visualizing and evaluating the expected bloodglucose values and predicted errors for use in the system of FIG. 1.

FIG. 8 is a flow diagram showing a routine for propagating anincremental change in medication dosing for use in the method of FIG. 2.

FIGS. 9 and 10 are user interface diagrams showing, by way of example,interactive screens for modeling incremental changes in medicationdosing for use in the system of FIG. 1 respectively before and aftersuperimposing the target ranges.

FIG. 11 is a user interface diagram showing, by way of example, aninteractive screen for a circadian profile for use in a furtherembodiment of the system of FIG. 1.

DETAILED DESCRIPTION

Ideal glycemic control in a diabetes patient occurs when the averagevalue of self-measured blood glucose (SMBG) at each point in a circadiandiabetes profile falls within a specific target range. The efficacy ofcurrent diabetes control when using a blood glucose meter (“glucometer”)can be improved to help make possible ideal glycemic management byharnessing the statistical properties of blood glucose and biologicrhythmicity, when represented as categorical, not time series, data, topredict circadian profiles of expected blood glucose values. FIG. 1 is ablock diagram showing a glucometer 12 for improving glucose managementthrough modeling of circadian profiles, in accordance with oneembodiment. Circadian profiles close the heretofore-incomplete circle ofcare and remove the danger of clinical diabetes medication prescriptionerrors, which have been caused by overly retrospective glycemic focusand chiefly by making adjustments to medications, typically focused oninsulin.

Physically, a glucometer 12 and a personal or laptop computer 13, or,alternatively, a mobile computing device, such as a smart phone,together form a diabetes management system 10, which provides guidancethat helps a patient 11 improve glycemic control. The system 10 requirestwo principal software components to manage diabetes. First, apatient-oriented structured database 14 is implemented on a glucometer12, which stores and organizes SMBG values, medication dosing for alltypes of anti-diabetes drugs, including insulin and oral agents, andrelated information into a circadian profile database. Second, acaregiver-centric consultation program 15 executes on a personal orlaptop computer 13 or mobile computing device. The program 15 generatespredictive circadian profiles for use in following diabetes patients andensuring their CG&MT compliance, but without the dilemma oftreatment-induced increased hypoglycemic risk.

The glucometer 12 is a “smart” suitably-programmed glucometer thatincludes an internal memory that electronically records the results ofeach blood glucose test, along with the date and time of testing, intothe database 14. Additionally, the glucometer 12 has a visual display 19and a set of input controls 18, such as buttons, that together form auser interface through which SMBG testing and diabetes medication dosingdata, as well as other optional but useful patient information, can beentered. The glucometer 12 is capable of collecting and storing thepatient's diabetes management data onboard for later offload to thepersonal or laptop computer 13 or mobile computing device via a built-indata interface port 17, such as a USB interface plug or other wirelessor wired adapter. In a further embodiment, the glucometer 12 is providedas an external “glucophone,” that is, a smart phone that has a built-inor add-on glucometer, and the database 14 and the program 15 are storedon and maintained by the glucophone.

When in use by the patient 11, the glucometer 12 calculates and displaysblood glucose level by reading a disposable test strip 16 upon which thepatient 11 has placed a drop of blood. With the reading of the teststrip 16, the patient 11 uses the input controls 18 to validate and thenidentify the current category of meal period, for instance,pre-breakfast. The SMBG measurement is then stored by the glucometer 12into the patient's circadian profile under the indicated meal periodcategory. Optionally, the patient 11 can also enter other patientinformation into the glucometer 12, such as physical activity, diet andstress at each meal period, and daily body weight. The patient's dataare internally stored in the database 14 on the glucometer 12, which issecured, private and password-protected, and both current and previouslystored data can be accessed.

Both the database 14 and program 15 are stored on the glucometer 12.Alternatively, only the database 14 need be stored on the glucometer 12and the program 15 can be stored and distributed separately, such as ona non-transitory computer-readable storage medium. The database 14 andprogram 15 could also be stored on a portable media device 12, such as aUSB flash drive or other form of non-transitory removablecomputer-readable storage medium, such as described in commonly-assignedU.S. Pat. No. 8,744,828, issued on Jun. 3, 2014, the disclosure of whichis incorporated by reference.

To offload the database 14 and execute the program 15, the glucometer 12is interfaced with a suitably-programmed computer, such as the personalor laptop computer 13, mobile computing device, or other compatiblecomputing device, which then loads the necessary program, library anddata files. For instance, when implemented with a USB interface plug,the glucometer 12 is inserted into a USB port on the personal or laptopcomputer 13 or mobile computing device. Once installed, the database 14and program 15 are personalized, if not already personalized by usingthe glucometer's user interface, with the patient's and his caregiver'sdemographic information. The patient 11 can then optionally enteradditional recent SMBG values, lifestyle, and diabetes medicationdetails for all types of anti-diabetes drugs, including insulin and oralagents, into the database 14. His caregiver performs a similar offloadand installation process on his computer and executes the program 15,which provides circadian profile-based predictions of blood glucosevalues and their expected errors, incremental suggestions and modelingof changes to medication dosing, and diabetes patient counseling points.Alternatively, the functionality of the program 15 could be providedthrough a so-called “cloud” computing infrastructure, in which patients'diabetes management data are stored online over a wide area public datanetwork, such as the Internet, or other network infrastructure and theprogram 15 can be remotely executed as a Web-implemented application orsmart phone “app,” such as described in commonly-assigned U.S. Patentapplication publication No. 2014/0032194, pending, the disclosure ofwhich is incorporated by reference.

The database 14 and program 15 collaboratively facilitate theachievement of improving glycemic management by respectively chroniclingrelevant patient self-management efforts with the assistance of theglucometer 12 and predictively modeling glycemic outcomes for caregiverreview and utilization. FIG. 2 is a flow diagram showing acomputer-implemented method 20 for improving glucose management with aglucometer through modeling of circadian profiles, in accordance withone embodiment. The method 20 can be implemented in software, such asthrough the database 14 and program 15, and execution of the softwarecan be performed on a computer system 10, such as described supra withreference to FIG. 1, as a series of process or method modules or steps.

By way of overview, a patient's SMBG measurements and accompanyingdosing of diabetes medications, including insulin and oral agents, areentered into a circadian profile that is stored in the structureddatabase 14 on the glucometer 12. The circadian profile is implementedusing a format that affords a one-to-one correspondence with the CG&MTmandated target ranges of blood glucose values and is organized as datarecords in the database 14. The circadian profile structures daily SMBGmeasurements and medication dosing into a data series of pre-meal andtimed post-meal categories. A day is modeled as a complete data series,even though the actual patient data within a particular “day” mayactually have been collected on different calendar days falling withinthe observational time frame. In one embodiment, each modeled day isdivided into meal periods for breakfast, lunch and dinner, and oneadditional “meal” period from pre-bedtime through overnight topre-breakfast, which is actually a period of fasting. Each data seriesincludes one pre-meal SMBG value and diabetes medication dosing for eachof breakfast, lunch, and dinner (three SMBG values) and one timedpost-meal period SMBG value also for each of breakfast, lunch, anddinner (three more SMBG values), plus one timed post-meal period SMBGvalue both pre-bedtime and overnight. In addition, notations on dailylifestyle chronicling physical activity, diet and stress at each mealperiod, and daily body weight can be included in the data record. Stillother patient- and treatment-related data can also be stored in thedatabase 14 on the glucometer 12.

The program 15 implements a statistical engine that regards the bloodglucose values as categories and not as a time series, that is, temporalevents based on actual “clock” time. A time series creates a time vectorproblem. For example, consider the averaging of continuous diurnalglucose readings for a patient's breakfast. On one day, say, Saturday,breakfast may occur at 6:30 am, while the next day, the patient decidesto sleep in and breakfast may consequently occur at 8:15 am. The lateroccurrence of Sunday's breakfast at 8:15 am causes that diurnal glucosereading to temporally coincide with the peak post-meal diurnal glucosereading of Saturday, which causes the averaging of the wrong bloodglucose values. To avoid the time vector problem, the blood glucose datais transformed from a time series axis to a category axis, where thecorrect pre- and timed post-meal blood glucose values are collectedaccording to their descriptive labels, not clock time. The use ofcategories enables the blood glucose value to be both predictable andmodelable using a log-normal statistical distribution for a model day.

A further advantage of using a category axis is being able toautomatically synchronize bolus and basal doses to both span the timeperiod beginning at just before a meal period all the way through tojust before the next meal period. Conventional insulin pumpmanufacturers often program basal rates according to clock times thatmay not necessarily correspond to pre-meal times. Diabetes patients 11who use an insulin pump are typically given the freedom to change theirmeal times or even skip meals altogether. Consequently, a potentiallyhazardous situation could arise when a patient 11 delays or skips a mealthat normally includes an increase or “jump” in basal rate. If thechange in basal rate was triggered by the patient 11 indicating the realstarting point of a meal, patient safety would be restored because thebasal rate would not change until the actual start of the meal.

The formation of a circadian profile begins with collecting meal perioddata into the database 14 with the glucometer 12 (step 21), as furtherdescribed infra with reference to FIG. 3. Meal period data collectionincludes both making SMBG measurements from a blood sample andidentifying any diabetes medications dosed, including insulin and oralagents, for the patient 11 for meal periods occurring over a recentobservational time frame, typically from the last seven days. The mealperiod data is then organized into a circadian profile (step 22), asfurther described infra with reference to FIG. 4. Meal period data iscumulatively collected from the patient 11 (step 23). Additional data isaccepted from the patient 11 and preferably at least two typicalmeasurements of pre-meal and post-meal SMBG are eventually collected foreach meal period. Upon completion, the meal period data, including thecompleted circadian profile, is offloaded from the glucometer 12 ontothe personal or laptop computer 15 or mobile computing device forprocessing by the program 14 (step 24).

The short-term, typically 7-day, time frame over recent glycemicmanagement provided by the circadian profile has been shown to allowaccurate prediction of blood glucose outcomes. As a result, a model ofthe expected values of near-term blood glucose values and theirpredicted errors can be created and visualized (step 25), as furtherdescribed infra with reference to FIG. 5. The visualization identifiesthose meal periods that are accompanied by a predicted risk ofhypoglycemia or occurrence of hyperglycemia, which the caregiver isurged to address with the patient 11 during consultation. In addition,the CG&MT target ranges or, if preferred, the caregiver's targets forthe patient 11, can be superimposed over the visualized blood glucoseprediction to enable the caregiver to evaluate likely excursions fromwell-managed glycemic care (step 26), as further described infra withreference to FIG. 6.

Through the visualized glycemic outcome model, incremental suggestionson possible changes to medication dosing can be provided, which thecaregiver can interactively explore to evaluate likely near-term affecton the patient 11. The program 15 supports the interactive explorationand modeling of all manner of anti-diabetes drugs, including insulin,other injectable medications and oral agents. As selected by thecaregiver, potential changes in medication dosing are visuallypropagated over the blood glucose prediction (step 27), as furtherdescribed infra with reference to FIG. 8. Other steps to further thepatient consultation are possible, such as reviewing weight controlthrough body mass index calculation and body weight trend analysis.

Meal periods form a set of categories within which SMBG values anddiabetes medication, including insulin and oral agents, are stored andstatistically analyzed. FIG. 3 is a flow diagram showing a routine 30for collecting meal period data into the circadian profile stored on theglucometer 12 for use in the method 20 of FIG. 2. First, the level ofblood glucose is read by the glucometer 12 from a blood sample providedby the patient 11 on a disposable test strip 16 (step 31), thendisplayed to the patient 11 on the visual display 19. Each of the SMBGvalues is systematically validated (steps 32-38), as follows. To ensureaccurate prediction of glycemic outcome, only recent and typical SMBGvalues are allowed. Recent (step 33) means that the SMBG value wasobtained during the seven days preceding the next caregiverconsultation. Other time frames are possible, but increasing the windowbeyond seven days undermines the value and meaningfulness of the SMBGdata as reflective of current actual glycemic condition. In a furtherembodiment, accuracy is ensured by applying a date and time stamp toeach SMBG value using a clock that is integral to the glucometer.However, the patient 11 can override the automated time and date stampto label atypical SMBG values. Typical (step 34) means that each of theSMBG values is without qualifications or exception. For instance, anSMBG measurement taken following a substantial Thanksgiving Day feastwould be atypical and would not be representative of the patient'stypical diet.

When entering data, the patient 11 has the ability to flag SMBG values(step 39) as not being either recent (step 33) or typical (step 34)either by performing a point-and-click operation with his mouse or otherpointing device, or by manually typing comments in an editable commentsfield in the circadian profile. The patient 11 also identifies theapplicable meal period category, for instance, pre-breakfast, and theSMBG value is retained (step 35). The ability to flag atypical SMBGvalues enables a patient 11 to associate a particular SMBG value withone or more events that can help explain the departure from expected andtypical SMBG levels, such as a high or low carbohydrate intake, exerciseor physical activity, or stress, as further described below withreference to FIG. 11. These explanatory events can be graded in levelsrelative to their normal baseline. In a further embodiment, flaggedatypical SMBG values can be differentially weighted for use in thedetermination of expected blood glucose values and predicted errors, asfurther described infra, discarded or used in any other way.

If the SMBG value is both recent and typical, which can be confirmed bythe patient 11 using the input controls 18 on the glucometer 12, thepatient 11 is prompted by the glucometer 12 through the visual display19 to identify the applicable meal period category, for instance,pre-breakfast, and the SMBG value is retained (step 35). Data entry canbe done all at once, or episodically, as convenient. As the program 15can model insulin and most oral (tablet) or injected anti-diabetesdrugs, the patient 11 also identifies using the input controls 18 anydiabetes medications, including oral or injected anti-diabetic agentsand insulin doses, which were taken or administered about the time thatthe blood glucose was measured by the glucometer 12 (step 36). Bothbasal and bolus insulin dosing, plus, optionally, the site of insulininjection on the patient's body, are identified. Insulin injection siteprovides a point of discussion between the caregiver and the patient 11during consultation in light of the affect that injection site can haveon insulin absorption and therefore the rate of glycemic regulation. TheSMBG value and the diabetes medication dosing are stored directly intothe database 14 by the glucometer 12 under the meal period category thatwas identified by the patient 11 (step 37). In a further embodiment,SMBG values and the diabetes medication dosing from a differentglucometer can be entered, for instance, to accommodate situations wherea patient has multiple glucometers for use at home, during work, whiletraveling, and so forth.

In one embodiment, only a single type of basal insulin, that is,longer-acting insulin with a physiologic mechanism of action principallyspanning one half day to no more than one full day, and a single type ofbolus insulin, that is, shorter-acting insulin with a physiologicmechanism of action principally spanning no more than three to eighthours, are accepted into the circadian profile. Other types oflonger-acting and short-acting drugs in addition to or in lieu ofinsulin could also be accepted. However, dosing of different types ofinsulin having the same temporal mechanism of action, such as multiplesimultaneous or overlapping short-acting insulin, is not permitted, asthe net affect of arbitrarily combinable multiple insulin dosing isambiguous and cannot be modeled with sufficient predictive certainty.

In a further embodiment, glucose lowering drugs, including shorter-,intermediate-, and longer-acting classes of anti-diabetes drugs,particularly oral hypoglycemia drugs, are modeled in addition to or inlieu of insulin. These medications include insulin sensitizers,including biguanides and thiazolidinediones; secretagogues, such assulfonylureas and non-sulfonylurea secretagogues; alpha-glucosidaseinhibitors; and peptide analogs, for instance, injectable incretinmimetics, injectable Glucagon-like peptide analogs and agonists, gastricinhibitory peptide analogs, dipeptidyl Peptidase-4 inhibitors, andinjectable Amylin analogues. Other types of glucose lowering medicationscould also be accepted.

Finally, a minimum of two SMBG values per meal period are needed to forma complete circadian profile, one pre-meal measurement and one timedpost-meal measurement, which, for statistical purposes, should berepeated a minimum of two times apiece for a total of 16 SMBG values,although more data, up to the maximum possible over a recent time frame,are possible. In one embodiment, a maximum of 56 SMBG values can beaccepted, which account for one pre-meal SMBG value for breakfast,lunch, and dinner (three SMBG values) and one timed post-meal periodSMBG value also for breakfast, lunch, and dinner (three more SMBGvalues), also a bedtime and an overnight (two SMBG values), over anentire seven-day week. The patient 11 continues to enter SMBG data (step40) until all available data up to that time have been entered.

Conventional approaches to diabetes management are often retrospectivein that changes in treatment are primarily based on historical, ratherthan recent, glycemic outcomes. In contrast, a circadian profile, asdescribed herein, shifts the focus to recent indicators of glycemiccondition and only for typical meal periods, which enables accurateprediction of short-term blood glucose and A1c outcomes. FIG. 4 is auser interface diagram showing, by way of example, an interactive screen50 for a circadian profile 51 for use in the system 10 of FIG. 1.Although the glucometer 12 stores the patient's diabetes management datadirectly into a circadian profile in the database 14 for later offloadto a personal or laptop computer 13 or mobile computing device, theinteractive screen 50 can be generated by the program 15 for use by boththe patient 11 and the caregiver during consultations to review, correctand fine tune the collected diabetes management data. In a furtherembodiment, the circadian profile can be uploaded back into the database14, as further described infra.

The circadian profile fits within the “three-legged stool” metaphor ofclinical diabetes management that focuses on body weight, A1c level andglycemic management. The creation of each circadian profile begins withassembling and organizing SMBG values and diabetes medication, as wellas other relevant information that is stored into the database 14. Thepatient's and caregiver's demographics 52 are entered as an initialstep. The remainder of the circadian profile 51 contains patientinformation that is organized under a series of pre-meal and timedpost-meal categories 53. In one embodiment, eight categories 53 of mealperiods are defined for breakfast, lunch and dinner: pre- and timedpost-meal, pre-bedtime and overnight periods, although othercategory-based series are possible, including mid-meal periods. Withineach category 53, the patient's body weight, SMBG values 54 and theirtimes of measurement are entered, plus any diabetes medication 55 thatwas taken or administered. In addition, for those patients who are oninjections of insulin, the site of injection is also entered, whichprovides a talking point during patient consultation. Finally, thepatient 11 can enter optional comments 56 on lifestyle, includingcarbohydrate estimate (“CHO”), exercise or physical activity level(“EX”), stress, and so forth. The lifestyle comments are also points ofpossible discussion with the caregiver. Other patient data can also becollected, like blood pressure and resting heart rate.

The categorization of recent typical SMBG values into a circadianprofile enables accurate prediction and modeling of near-term bloodglucose and A1c levels. FIG. 5 is a flow diagram showing a routine 60for visualizing expected blood glucose values and predicted errors foruse in the method 20 of FIG. 2. Each of the sets of meal period data isevaluated and modeled (steps 61-64), as follows. The expected bloodglucose value and predicted error for each meal period on the categoryaxis is first determined and a model of the expected blood glucosevalues and their respective predicted errors by meal periods is createdfor a model day (step 62). During the statistical determination of theexpected blood glucose values and predicted errors, all SMBG values maybe treated as having equal weight in terms of their respective influenceon the prediction and modeling of near-term blood glucose and A1clevels. In a further embodiment, individual SMBG values can be flaggedand differentially weighted based on a weighting criteria, such as usedto flag atypical SMBG, as discussed supra, which causes the model toreflect the relative influence of each SMBG value based on itsrespective weight. Other ways of emphasizing or deemphasizing factorsaffecting SMBG monitoring are possible.

A seven-day window is used to generate the model. Recall that areplicated minimum of two SMBG values per meal period is preferred,although more data within the seven-day observational time frame arebelieved to improve accuracy. The statistical methods for performing thenear-term blood glucose level prediction has been clinically validatedfor both efficacy and safety, such as described in A. M. Albisser etal., Home Blood Glucose Prediction: Validation, Safety, and EfficacyTesting in Clinical Diabetes, Diabetes Tech. Ther., Vol. 7, pp. 487-496(2006); and A. M Albisser et al., Home Blood Glucose Prediction:Clinical Feasibility and Validation in Islet Cell TransplantationCandidates, Diabetologia, Vol. 48, pp. 1273-1279 (2005), the disclosuresof which are incorporated herein by reference.

Empirically and as scientifically demonstrated supra, when assembledinto distinct pre- and timed post-meal categories, SMBG data follows alog-normal distribution. Consequently, the expected blood glucose valueand predicted error for each meal period are visualized using alog-normal distribution (step 63), as further described infra withreference to FIG. 7. Statistically, each expected blood glucose value isthe geometric mean of the SMBG values stored in the database 14 for theobservational time frame and the predicted error is the standarddeviation of the geometric mean. When the patient's blood glucose andA1C values are within target range, the type of statistical distributionused in the model becomes less crucial. As a result, in a furtherembodiment, a standard normal distribution can be used instead of alog-normal distribution. Under the same rationale, still other types ofstatistical distributions could also be used.

After all of the sets of meal period data have been evaluated andmodeled, an A1c estimate is determined (step 65) for inclusion with thevisualization. In one embodiment, the patient's A1c is derived from meanSMBG values, such as described in C. L. Rohlfing et al., Defining theRelationship Between Plasma Glucose and HbA(1c): Analysis of GlucoseProfiles and HbA(1c) in the Diabetes Control and Complications Trial,Diabetes Care, Vol. 25(2), pp. 275-8 (2002), the disclosure of which isincorporated by reference.

With a circadian profile 51 for a diabetic patient 11, a caregiver isable to apply a “treat to target” approach, as presented through avisual display of glucose management data, that focuses on moving thepatient's SMBG values into target ranges that represent recognizedwell-managed glycemic control, as opposed to merely keeping A1c below acertain point. FIG. 6 is a flow diagram showing a routine 70 forsuperimposing target ranges over the expected blood glucose values andpredicted errors for use in the method 20 of FIG. 2. The one-to-onecorrespondence between the meal periods in each circadian profile 51 andthe CG&MT mandated target ranges enables the expected blood glucoselevels and the target ranges to be visualized together.

As an initial step in the approach, the target blood glucose levelranges for each meal period are determined for the diabetes patient 11(step 71). The target ranges are then visually superimposed over theexpected blood glucose levels and the ranges (step 72). The targetranges can either be from the CG&MT or as specified by the caregiver. Inone embodiment, different sets of target ranges can be used, including a“default” target range, a gestational diabetes target range (women only)and a target range for use in breaking insulin resistance. The defaulttarget range specifies a pre-meal target of 80 mg/dL<SMBG value<140mg/dL and a post-meal target of 80 mg/dL<SMBG value<180 mg/dL,regardless of whether the meal period is breakfast, lunch or dinner. Thegestational target range decreases the pre-meal target range to 60mg/dL<SMBG value<120 mg/dL. The insulin resistance target range raisesthe pre-meal target to 120 mg/dL<SMBG value<180 mg/dL, which has theaffect of providing the patient 11 with a reason to reduce medicationdosing by moving his SMBG values into the (raised) target range, insteadof continually increasing medication in a futile attempt to reach themandated target range. Once the insulin-resistant diabetes patient 11has achieved the raised insulin resistance breaking target, the defaulttarget range can again be approached in steps.

The treat-to-target approach is equally applicable to controllinghyperglycemic occurrence and hypoglycemic risk, where medication mustrespectively be increased or decreased. The expected blood glucose levelin each meal period is respectively compared to hypoglycemic andhyperglycemic thresholds (steps 73 and 75) and, if a risk exists, themeal period is highlighted and a notice is displayed to inform thepatient 11 and his caregiver (steps 74 and 76). In one embodiment, ahypoglycemic threshold of 50 mg/dL and a post-meal hyperglycemicthreshold of 180 mg/dL are used, where an expected blood glucose levelfalling outside of either threshold will trigger an appropriate warning.The treat-to-target approach also dovetails well with dietaryeducational efforts in which the patient 11 is taught to either decreaseor increase carbohydrate intake to respectively avoid onset ofhyperglycemia or hypoglycemia.

The treat-to-target approach is facilitated through a graphicalvisualization of the model of the expected blood glucose values andpredicted errors with mandated target ranges superimposed. FIG. 7 is auser interface diagram showing, by way of example, an interactive screen80 for visualizing and evaluating the expected blood glucose values andpredicted errors for use in the system 10 of FIG. 1. Since the program15 does not make changes to the patient's course of treatment per se andonly provides guidance, the screen 80 can be used by both the patientand the caregiver, as well as other users.

The visualization groups the expected blood glucose values and theirrespective predicted errors in the model 81 by meal periods 82 for amodel day. The model 81 represents the patient's expected blood glucosevalues 83 and predicted errors 84 before predicted affects of medicationdosing increments. Each of the meal periods 82 includes categories 85,86 for a pre-meal and a timed post-meal expected blood glucose value 83and a predicted error 84. Due to the log-normal distribution, thepredicted error 84 above and below an expected blood glucose value 83 isnot symmetric and a wider predicted error 84 appears above each expectedblood glucose value 83 than below. The target ranges 87 are superimposedover each of the expected blood glucose values 83. When the probabilityrisk is greater than 5%, or other selectable range, that the expectedblood glucose values 88, 89 will fall below the hypoglycemic threshold,the risk is flagged with a warning 90 displayed to the user. As aresult, those meal periods where predicted blood glucose values may fallout of target range can be readily identified by the caregiver andpatient alike. Finally, the estimated A1c 91 derived from mean SMBGvalues is displayed.

The visualization of the expected blood glucose values and predictederrors, and target ranges provides a starting point for the caregiver tobegin working with the patient 11. Changes to diabetes medication,particularly medication dosing, may be necessary to move the SMBG valuesinto target range. FIG. 8 is a flow diagram showing a routine 100 forpropagating an incremental change in medication dosing for use in themethod 20 of FIG. 2. The expected glucose values, as modified by thepharmacodynamics of any medication dosing changes contemplated for thepatient 11, enable the program 15 to suggest quantitative changes tomedication dosing for caregiver or patient 11 consideration. The program15 can also generate qualitative feedback. Generating quantitativefeedback provides a closed-loop treatment model. For safety, allowableincremental changes in medication dosing are limited in amount toachieve the targets more slowly, but safely, and without the risk oflimit cycling. However, to accommodate the slower physiological responseof these smaller incremental changes, the patient 11 has to measure SMBGmore often, up to six times per day, than with a qualitative approachthat works with fewer SMBG values.

The amount of change in medication dosing, based on the diabetesmedications already identified by the patient 11, can be determined byeither incrementally increasing or decreasing the amount of the dosedmedication in the model 81 (step 101). The pharmacodynamics of thediabetes medication is applied in proportion to the incremental changein dosing, as incrementally increased or decreased (step 102), and thevisualization is dynamically adjusted by adding the incremental increaseor decrease in blood glucose level to the expected blood glucose values83 (step 103). The program 15 uses the pharmacodynamics of the diabetesmedications to model the affect on the expected values of near-termblood glucose values and their predicted errors. Drug manufacturersformulate their drugs, so that an incremental change in dosing,amounting to the smallest dosing unit, such as a half tablet of an oralmedication of the lowest strength or one IU of an injectable medication,produces a glucose lowering effect similar to all the otheranti-diabetes drugs in its class. This “normalization” is used to avoidhaving their drug require different dosing profiles when compared tocomparable drugs offered by their competitors, where, for instance, onemanufacturer's medication may require three oral tablets while acompetitor's medication only requires a single oral tablet.

The normalization of comparable anti-diabetes medications is reflectedin the visualization, which allows a user to change medication dosingincrementally (steps 101-103) until the expected blood glucose valuesmove into the target ranges (step 104). The pharmacodynamics allow one“click” on the user interface to reflect a similar glucose loweringaffect for all anti-diabetes drugs in the same class, although thepharmacodynamics of different drug classes are applied in such a way asto normalize the area under the response curves to reflect the totaldrug administered. As a result, longer-acting drugs have a lower peak,but last longer to keep the area under the blood glucose curve similarto the same amount of a shorter-acting drug, which has a higher peak andshort duration of action.

That amount of incremental increase or decrease in the dosed medicationis then presented to the caregiver as an incremental suggested change inmedication dosing (step 105). The qualitative scale is slight, moderate,or significant, although in a further embodiment, this scale can also beexpressed quantitatively. Specifically, the program 15 scales six leftwhole-clicks or twelve right half-clicks to span the range from slightqualifiers (+), to moderate qualifiers (++), and finally to significantqualifiers (+++) for all dosage increments, or decrements, which allows“clicks” to be quantified into usable measures of dosing, such as halforal tables or IUs of insulin. In addition, the program 15 provides amechanism for simply “accepting” or documenting medication changes, suchthat those changes that are pre-populated when the patient 11 entersSMBG readings and verifies the doses taken.

In a further embodiment, the circadian profile and incremental increaseor decrease in the dosed medication could be uploaded back onto theglucometer 12, depending upon whether the glucometer 12 is used as adosing treatment controller, where the incremental changes are uploaded,or a regimen guidance tool, in which the incremental changes aremaintained separately and offline from the glucometer 12. The decisionon how to use the glucometer 12 in diabetes management and uploading theincremental changes into the onboard database turns on whether suchusage is thought of as being inside or outside the treatment arc thatconnects the output of the glucometer 12 to the “tip of the needle,”that is, the drug delivery device. The incremental changes would beuploaded back on to the glucometer 12 if the onboard database isintended to be portable and the patient 11 intends to run the program 15on any personal or laptop computer 13 or mobile computing device withoutthe benefit of a virtualized database provided through a “cloud”computing infrastructure. However, incremental change uploading canpotentially become cumbersome if the patient 11 has more than oneglucometer 12 actively in use, such as at home and at work, and keepingthe databases on each of those devices current could become a logisticalchallenge to the patient 11 and his caregiver.

Throughout exploration of potential medication dosing changes, includinginsulin and oral agents, the possible affect of any suggested change, orother amount of change in medication dosing desired, is added to thevisualization by dynamically adjusting the expected glucose values basedon the relative pharmacodynamics of the new medication dosing change.FIGS. 9 and 10 are user interface diagrams showing, by way of example,interactive screens 110, 120 for modeling incremental changes inmedication dosing for use in the system 10 of FIG. 1 respectively beforeand after superimposing the target ranges. Referring first to FIG. 9,with the new medication dosing, the two expected blood glucose values111, 112 that formerly risked hypoglycemia at a 5%, or greaterprobability are now safely raised and the risk of hypoglycemia has beenremoved. In addition to the actual medications described (Apidra andLantus), the fields in the descriptor bar for meal periods 82 includeplaceholders at the breakfast, lunch and dinner meal periods forlonger-acting (insulin) medications (“LamB,” “LamL,” “LamD”) and at onlythe bedtime “meal” period for shorter-acting (insulin) medication(“SamS”).

In a still further embodiment, a caregiver can model proposed changes tomedication dosing and, through the cloud storage infrastructure, feedthe results back to the patient's personal or laptop computer 13, mobilecomputing device, portable media device 12, glucometer 17 or other typeof portable blood glucose testing device with onboard data collectioncapabilities, as applicable. In addition, manufacturers of glucosemeters and glucose sensing strips that are able to monitor their devicescan also be participants to the caregiving process. These manufacturerscan provide continued calibration and other performance metrics thatwill assure quality, accuracy, and safety in their measuring devices andsignal an unsafe or unreliable status, which would flag a reading aspossibly atypical or unreliable.

Changes to the dosed medications, whether insulin or oral agents, can beexplored by the user through a control panel 113 (labeled “±Doses toCorrect Rx”). Within the control panel 113, controls 114, 115 under thelabel “Delta” respectively allow the user to explore incrementallyincreasing or decreasing the shorter-acting and longer-actingmedications for a meal period 82, as indicated by a connector line 116.The sub-control button (labeled ‘R’) serves as a shortcut to reset anyexplored increments back to zero. Here, the breakfast meal period 117 isselected with the shorter-acting medication set to Apidra and thelonger-acting medication set to LamB, which is, an as-yet unspecified,longer acting medication at B. To explore the impact of medicationdosing changes during other meal periods 82, for instance, the lunchmeal period 118, the user selects the area labeled “Lunch,” upon whichthe lunch meal period 118 is connected by the connector line 116 to thecontrol panel 113 and the breakfast meal period 117 is deselected.

In one embodiment, the change in insulin dosing can be presented instandard dosage IUs (International Units), in increments of tenths of anIU, where the scaling is rationalized for the patient's delivery device.For instance, hypodermic syringes have a scale that depends on theirfull volume, whereas insulin injection pens dose in increments of 1 IUor 2 IU per click. Insulin pumps are capable of doing in increments of0.1 IU. However, in practice, insulin dosing can be course when the doseis over 10 IU and finer for infants whose dose could be ˜1 IU, such as1.5 IU or 0.5 IU. In suggesting the final change in insulin dosing tothe patient 11 or caregiver, the quantitative dose suggestion couldfollow a conversion of clicks for an insulin injection pen or some otherindividualized scaling factor that depends on the size of the totaldaily dose. Quantifying the clicks to tablets conversion could be by 0.5tablets up to a maximum of 1 to 3 tablets, or limited by the maximummeal and daily allowable amounts.

The expected blood glucose value 83 and predicted error 84 for each mealperiod 82 are adjusted for the pharmacodynamics of the changes to thediabetes medication being explored. Typically, the pharmacodynamicsfollow the dose-response characteristic. The pharmacodynamics define theeffect of the drugs, that is, the patient's diabetes medication, onblood glucose. The pharmacodynamics of each type of drug is availablefrom the manufacturer. Beginning with the meal period at which thediabetes medication change was administered, the pharmacodynamics areused to raise or lower the expected level of blood glucose in thevisualization until the propagated pharmacodynamics are fully exhausted.Depending upon the particular drug's pharmacodynamics, the expectedblood glucose levels in a sequence of several adjacent meal periods maybe affected. For instance, insulin glargine taken as a basal dose islong-acting and the pharmacodynamics will affect meal periods forseveral days, although the insulin's ability to lower blood glucoselevel after the first 24 hours is significantly diminished. As well,insulin taken as a pre-meal bolus dose is short-acting, yet thepharmacodynamics may well equally propagate for an entire day, albeit ofrelatively small continuing blood glucose level-lowering affect.However, the cumulative pharmacodynamics of all of the basal doses andeach of the bolus doses taken throughout the observational time framemay nevertheless lower the expected blood glucose level at any givenmeal period more than a single bolus dose would if taken at that samemeal period in isolation from any other insulin doses.

Following (or during) the exploration of changes to the medicationdosing, including insulin or oral agents, the target ranges 87 can besuperimposed to provide visual guidance as to whether the new medicationdosing will satisfactorily move the expected blood glucose values 83into the mandated targets and avoid both the risk of hypoglycemia andoccurrence of hyperglycemia. Referring next to FIG. 10, the targetranges 87 have been superimposed above the expected blood glucose values83 and predicted errors 84. All of the patient's expected blood glucosevalues 83 are within target and reflect ideal glycemic control. Inaddition, the caregiver is able to also ensure proper dosing ofmedications through a set of prescription checkers steps (“RxChecks”)121 that includes a control 122 to “Check for possible medicationoverdoses,” which checks that medication is dosed within safe limits ateach meal period and for the entire day. Other types of prescriptionchecks and safeguards are possible.

Atypical SMBG values can also serve to guide the medication dosingadjustment processes. FIG. 11 is a user interface diagram showing, byway of example, an interactive screen 130 for a circadian profile foruse in a further embodiment of the system 10 of FIG. 1. The SMBG values131 and their times of measurement are entered along with an explanation132 that flags an atypical SMBG value for the lunch meal period. Otherlabels within the various interactive screens, such as the labelaccompanying RxChecks steps 121 (shown in FIG. 10), can be highlightedto call attention to unusual events that may lead to atypical SMBG data.Atypical or “unusual” events touch on aspects of the patient's diet,exercise, physical activity, stress, and similar often unavoidableoutcomes of activities of daily living, for example, eating an atypicalamount of carbohydrates (either more or less than normal, as happens onThanksgiving Day) without a matching correction bolus, experiencing moreor less stress than usual, or engaging in an unusual amount of exerciseor physical activity.

In one embodiment, a flagged event triggers the display of a notice to atooltip associated with the associated post- and mid-meal glucoseranges, that is, “After L” and “Mid L-D.” Here, the notice would say,for instance, “Unusual±CHO or unusual activity in this MP may distortthis prediction.” Similarly, for the following pre-meal glucose range inthe following meal period, that is, “Pre D,” the notice would say, forinstance, “Preceding unusual±CHO or unusual activity may distort thisprediction.” Also, as a further guide in deciding whether to accept orignore a potentially atypical expected blood glucose value and itsrange, a tooltip can also be associated with the low end of thepredicted range 88 (shown in FIG. 7) for a meal period category, such as“Before L,” that includes the acceptable SMBG reading in the range forthat meal category. This tooltip notice can be helpful in understandingwhy a warning about a factitious risk of hypoglycemia that arises froman outlying hyperglycemia event can safely be ignored. Other types andtriggers of notice are possible.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-implemented method for facilitatingaccurate glycemic control by modeling blood glucose using circadianprofiles, comprising the steps of: defining a plurality of meal periodsthat each occur each day at a set time; defining a plurality of classesthat each class comprises a set of anti-hyperglycemic medications thathas similar glucose lowering effects; building a circadian profile for adiabetic patient, comprising the steps of: choosing an observationaltime frame for the circadian profile comprising a plurality of days thathave occurred recently; collecting at least two sets of pre- andpost-meal period data that were recorded at each of the meal periodsthat occurred each day in the observational time frame and stored on aglucose meter; reading a level of blood glucose on a test strip providedto the glucose meter by the diabetic patient for each of the mealperiods; identifying a dose of an anti-hyperglycemic medication that wastaken during each of the meal periods as the reading of the bloodglucose level; identifying one of the classes to which theanti-hyperglycemic medication is comprised; and storing the bloodglucose level and the anti-hyperglycemic medication dose into thecircadian profile in a record for each of the meal periods; and creatinga model of glucose management for the diabetic patient for each of theanti-hyperglycemic medications in the identified class, comprising thesteps of: defining a modeling period comprising a plurality of days,which each comprise the same plurality of the meal periods that occurredeach day in the observational time frame; estimating expected bloodglucose values and their predicted errors at each of the meal periodsfrom the blood glucose level in each record based on the meal periods inthe circadian profile that respectively occur at the same set times;visualizing the expected blood glucose values and their predicted errorsover time for each meal period in a log-normal distribution; andselecting one of the meal periods and, for each anti-hyperglycemicmedication in the identified class, modeling a change in the dose of theanti-hyperglycemic medication for the selected meal period, comprisingthe steps of: obtaining glucose lowering effect of the modeled change inthe dose of the anti-hyperglycemic medication and glucose loweringeffects of other anti-hyperglycemic medications in the identified class;normalizing the glucose lowering effect of the modeled change in thedose of the anti-hyperglycemic medication with the glucose loweringeffects of the other anti-hyperglycemic medications in the identifiedclass; propagating the normalized glucose lowering effect over time forthe modeled change in the dose of the anti-hyperglycemic medication tothe expected blood glucose values, beginning with the selected mealperiod and continuing with each of the meal periods occurringsubsequently in the modeling period, the normalized blood glucoselowering effect being adjusted in proportion to the set time of eachsubsequent meal period until the normalized blood glucose loweringeffect is exhausted; and visualizing the expected blood glucose valuesas propagated and their predicted errors in the log-normal distribution,wherein the steps of building the circadian profile are performed on asuitably-programmed glucose meter and the steps of creating the modelare performed on a suitably-programmed computer.
 2. A method accordingto claim 1, further comprising the step of: determining target rangesfor blood glucose at each of the meal periods occurring each day in themodeling period and superimposing the target ranges over the expectedblood glucose values for each meal period occurring each day in themodeling period in the log-normal distribution.
 3. A method according toclaim 1, further comprising the steps of: associating a flag with atleast one of the blood glucose levels as an atypical measurement; andprocessing the flagged blood glucose level in the circadian profilecomprising at least one of: treating the flagged blood glucose leveldifferently from other blood glucose levels; and discarding the flaggedblood glucose level from the circadian profile.
 4. A method according toclaim 3, further comprising the steps of: defining the atypicalmeasurement based on at least one of a level of carbohydrate intake,physical activity, and stress of the diabetic patient; determining aweighting criteria for the flagged blood glucose level; and assigning aweight to the flagged blood glucose level.
 5. A method according toclaim 1, further comprising the step of: determining the classes of theanti-hyperglycemic medications based on the glucose lowering effectcomprising at least one of short-acting, intermediate-acting, andlong-acting.
 6. A method according to claim 5, further comprising thestep of: identifying the glucose lowering effect by at least one ofhours, days, weeks, months, and years.
 7. A method according to claim 1,further comprising the step of: modeling a kind of anti-hyperglycemicmedication comprising at least one of oral anti-hyperglycemic medicationand injectable anti-hyperglycemic medication.
 8. A method according toclaim 1, further comprising the step of: defining the meal periods ascomprising, within each day in the observational time frame, breakfast,lunch, dinner, and bedtime meal periods.
 9. A method according to claim1, further comprising the step of: integrating further information intothe meal period data in the circadian profile comprising at least one ofa body weight of the diabetic patient, times of measurement of theself-measured blood glucose, site of injection of the anti-hyperglycemicmedication, and comments on lifestyle.
 10. A method according to claim1, further comprising the step of: providing the modeled change in thedose of the anti-hyperglycemic medication as dose suggestion comprisingat least one of relative dose suggestion and absolute dose suggestion.11. A method according to claim 1, further comprising at least one ofthe steps of: presenting a qualitative scale for the dose suggestioncomprising scales of slight change, moderate change, and significantchange; and presenting a quantitative scale of the dose suggestioncomprising a quantum of the anti-hyperglycemic medication.
 12. A methodaccording to claim 1, further comprising the steps of: including a bodyweight of the diabetic patient in the circadian profile; and performinga trend analysis of the body weight over any preceding observationaltime frames for the selection of the change in the dose of theanti-hyperglycemic medication.
 13. A blood glucose meter forfacilitating accurate glycemic control by modeling blood glucose usingcircadian profiles, comprising: an electronically-stored databaseimplemented on a glucose meter and comprising a plurality of records,each record comprising a circadian profile, comprising: a plurality ofmeal periods that each occur each day at a set time and divide eachcircadian profile into the meal periods; a plurality of classes thateach class comprises a set of anti-hyperglycemic medications that hassimilar glucose lowering effects; an observational time frame for thecircadian profile comprising a plurality of days that have occurredrecently; at least two of typical measurements of pre-meal and post-mealself-measured blood glucose that were recorded at each of the mealperiods that occurred each day in the observational time frame; a doseof anti-hyperglycemic medication that was taken during each of the mealperiods for which the blood glucose measurements were recorded; and oneof the classes to which the dose of the anti-hyperglycemic medication iscomprised; and an executable application stored on the glucose meter andconfigured to execute on a suitably-programmed computer to model glucosemanagement through the circadian profile for the diabetic patient foreach of the anti-hyperglycemic medications in the class, comprising: acollection module configured to offload the database from the glucosemeter and to collect the blood glucose measurements comprising each ofthe meal periods; a statistical engine configured to determine expectedblood glucose values and their predicted errors at each of the mealperiods from the blood glucose measurements based on the meal periods inthe circadian profile; a log-normal distribution module configured tovisualize the expected blood glucose values and their predicted errorsover time for each meal period in a log-normal distribution on thecomputer; and a change modeling module configured to select one of themeal periods and, for each anti-hyperglycemic medication in the class,to model a change in the dose of the diabetes medication for theselected meal period category, comprising: an effect module configuredto obtain glucose lowering effect of the modeled change in the dose ofthe anti-hyperglycemic medication and to obtain glucose lowering effectsof other anti-hyperglycemic medications in the class; a normalizationmodule configured to normalize the glucose lowering effect of themodeled change in the dose of the anti-hyperglycemic medication with theglucose lowering effects of the other anti-hyperglycemic medications inthe class; a propagation module configured to propagate the normalizedglucose lowering effect over time for the modeled change in the dose ofthe anti-hyperglycemic medication to the expected blood glucose values,beginning with the selected meal period and continuing with each of themeal periods occurring subsequently in the modeling period, thenormalized blood glucose lowering effect being adjusted in proportion tothe set time of each subsequent meal period until the normalized bloodglucose lowering effect is exhausted; and a visualization moduleconfigured to visualize the expected blood glucose values as propagatedand their predicted errors in the log-normal distribution.
 14. A bloodglucose meter according to claim 13, further comprising: a flag moduleconfigured to associate a flag with at least one of the recordedself-measured blood glucose measurements as an atypical measurement; anda flag process module configured to process the flagged self-measuredblood glucose in the circadian profile comprising at least one of: aflag weight module configured to treat the flagged self-measured bloodglucose measurement differently from other recorded self-measured bloodglucose measurements; and a deletion module configured to discard theflagged self-measured blood glucose measurement from the circadianprofile.
 15. A blood glucose meter according to claim 14, furthercomprising: a classification module configured to define the atypicalmeasurement based on at least one of a level of carbohydrate intake,physical activity, and stress of the diabetic patient; a criteria moduleconfigured to determine a weighting criteria for the flaggedself-measured blood glucose measurement; and a weight assignment moduleconfigured to assign a weight to the flagged self-measured blood glucosemeasurement.
 16. A blood glucose meter according to claim 15, furthercomprising: a threshold of at least one of hypoglycemic risk andhyperglycemic occurrence, which are both expressed as blood glucosevalues stored in the database; a warning module configured to identifyeach of the expected blood glucose values exhibiting either a risk offalling below the hypoglycemic risk threshold or rising above thehyperglycemic occurrence threshold; and a tooltip for indicating anotice associated with the atypical measurement on the display andexplaining a reason why the risk can be ignored.
 17. A blood glucosemeter according to claim 13, further comprising: target ranges stored inthe database for the expected blood glucose values at each meal periodin the model day; a target module configured to superimpose the targetranges over the visualized expected blood glucose values; a changecontrol on the control panel for at least one of increasing anddecreasing the dose of the anti-hyperglycemic medication for the mealperiod on the display, the increasing and decreasing of theanti-hyperglycemic medication being operated by clicking the changecontrol on the control panel; and an adjustment module configured toadjust the expected blood glucose values, comprising at least one of: adose increase module configured to increase the dose of theanti-hyperglycemic medication until the expected blood glucose value forthe meal period moves into the target range based on the normalizedglucose lowering effect of the change in the dose of theanti-hyperglycemic medication; a dose decrease module configured todecrease the dose of the anti-hyperglycemic medication until theexpected blood glucose value for the meal period moves into the targetrange based on the normalized glucose lowering effect of the change inthe dose of the anti-hyperglycemic medication; and a dose suggestionmodule configured to determine the changes in the dose of theanti-hyperglycemic medication as a dose suggestion.
 18. A blood glucosemeter according to claim 17, further comprising: a quantum moduleconfigured to define a quantum and unit of the anti-hyperglycemicmedication from the normalized glucose lowering effect of theanti-hyperglycemic medication applied to the expected blood glucosevalue as the dose suggestion, comprising: a count module configured tocount a number of the clicks of the change control on the control panelfor the adjustment of the expected blood glucose value into the targetrange; and a conversion module configured to covert the number of theclicks into a quantum of the anti-hyperglycemic medication.
 19. A bloodglucose meter according to claim 13, further comprising: a medicationclass module configured to determine the classes of theanti-hyperglycemic medications based on the glucose lowering effectcomprising at least one of short-acting, intermediate-acting, andlong-acting.
 20. A blood glucose meter according to claim 19, furthercomprising: a temporality module configured to identify the glucoselowering effect by at least one of hours, days, weeks, months, andyears.
 21. A blood glucose meter according to claim 13, furthercomprising: a medication kind module configured to model a kind ofanti-hyperglycemic medication comprising at least one of oralanti-hyperglycemic medication and injectable anti-hyperglycemicmedication.
 22. A blood glucose meter according to claim 13, furthercomprising: a meal period module configured to define the meal periodsas comprising, within each day in the observational time frame,breakfast, lunch, dinner, and bedtime meal periods.
 23. A blood glucosemeter according to claim 13, further comprising: an integration moduleconfigured to integrate further information into the meal period data inthe circadian profile comprising at least one of body weight of thediabetic patient, times of measurement of the self-measured bloodglucose, site of injection of the anti-hyperglycemic medication, andcomments on lifestyle.
 24. A blood glucose meter according to claim 13,further comprising: a suggestion module configured to provide themodeled change in the dose of the anti-hyperglycemic medication as dosesuggestion comprising at least one of relative dose suggestion andabsolute dose suggestion.
 25. A blood glucose meter according to claim24, further comprising at least one of: a qualitative scale for the dosesuggestion comprising scales of slight change, moderate change, andsignificant change; and a quantitative scale for the dose suggestioncomprising a quantum of the anti-hyperglycemic medication.